code: def generate_solvers(
      constraints, variables="x", nvars=None, locals=None
  ):
      """generate constraints solver functions from a set of constraint strings
  
      Args:
          constraints (str): a string of symbolic constraints, with one constraint
              equation per line. Constraints can be equality and/or inequality
              constraints. Standard python syntax should be followed (the ``math``
              and ``numpy`` modules are already imported). The left-hand side of
              each equation must be simplified to support assignment.
          variables (str, default='x'): desired variable name. A list of variable
              name strings is also accepted for when desired variable names
              don't have the same base, and can include variables that are not
              found in the constraints equation string.
          nvars (int, default=None): number of variables. Includes variables not
              explicitly stated in the constraints (e.g. ``x1`` in the example below).
          locals (dict, default=None): additional variables used in the symbolic
              constraints equations, and their desired values. Default is
              ``{'tol': 1e-15, 'rel': 1e-15}``, where ``'tol'`` and ``'rel'`` are
              the absolute and relative difference from the extremal value in a
              given inequality. For more details, see ``mystic.math.tolerance``.
  
      Notes:
          Alternately, ``constraints`` may be a tuple of strings of symbolic
          constraints. Will return a tuple of constraint solver functions.
  
          By default, the top-level namespace of ``numpy`` and ``math`` are
          imported into ``locals``.
  
      Examples:
          >>> constraints = '''
          ...     x2 = x0/2.
          ...     x0 >= 0.'''
          >>> solv = generate_solvers(constraints, nvars=3)
          >>> print(solv[0].__doc__)
          'x[2] = x[0]/2.'
          >>> solv[0]([1,2,3])
          [1, 2, 0.5]
          >>> print(solv[1].__doc__)
          'x[0] = max(0., x[0])'
          >>> solv[1]([-1,2,3])
          [0.0, 2, 3]"""
      if not isinstance(constraints, str):
          return tuple(
              generate_solvers(constraint, variables, nvars, locals)
              for constraint in constraints
          )
  
      _constraints = constraints_parser(
          constraints, variables=variables, nvars=nvars
      )
  
      # parse epsilon
      if locals is None:
          locals = {}
      locals["tol"] = tol = locals["tol"] if "tol" in locals else 1e-15
      locals["rel"] = rel = locals["rel"] if "rel" in locals else 1e-15
      if tol < 0 or rel < 0:
          msg = "math domain error"
          raise ValueError(msg)
      # default is globals with numpy and math imported
      globals = {}
      code = """from math import *; from numpy import *;"""
      code += """from builtins import *;"""  # don't overload builtins
      code += """from numpy import mean as average;"""  # use np.mean not average
      code += (
          """from mystic.math.measures import spread, variance, mean;"""
      )
      code += """from mystic.math.measures import impose_spread, impose_mean;"""
      code += """from mystic.math.measures import impose_sum, impose_product;"""
      code += """from mystic.math.measures import impose_variance;"""
      code += """from mystic.math import tolerance as _tol;"""
      code = compile(code, "<string>", "exec")
      exec(code, globals)
      globals.update(locals)  # XXX: allow this?
  
      # build an empty local scope to exec the code and build the functions
      results = {"solver": []}
      for func in _constraints:
          fid = str(id(func))
          fdict = {"name": fid, "equation": func, "container": "solver"}
          # build the condition function
          code = """
  def {container}_{name}(x):
      '{equation}'
      exec('{equation}')
      return x
  {container}_{name}.__name__ = '{container}'
  """.format(
              **fdict
          )  # XXX: better, check if constraint satisfied; if not, solve
          # XXX: should locals just be the above dict of functions, or should we
          #     add the condition to container then delete the condition?
          code += """
  {container}.append({container}_{name})
  del {container}_{name}""".format(
              **fdict
          )
          code = compile(code, "<string>", "exec")
          exec(code, globals, results)
  
      # XXX: what's best form to return?  will couple these with ctypes ?
      return tuple(results["solver"])
  
imports: from numpy import ndarray, asarray, any
  from mystic._symbolic import solve
  from mystic.tools import list_or_tuple_or_ndarray, flatten
label_desc: Use image node_id 24 for calling a global function with example usage: generate_solvers(constraints, variables, nvars, locals) and returns: tuple, tuple